SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Approximate Conditional Mean Particle Filter

Author(s)

Derek Yee	McMaster University
James Reilly	McMaster University
Thia Kirubarajan	McMaster University

Get the paper in PDF format

To obtain Acrobat Reader (version 5 minimum required) necessary to his read.

Abstract

We consider partially observed non-Gaussian dynamic state space models in which the process equation consists of a combination of linear and nonlinear states and the process noise for the nonlinear state update is distributed according to a mixture of Gaussians. In this paper, we solve a Bayesian filtering problem. The proposed filter is an efficient combination of the particle filter and the approximate conditional mean filter. Simulation results on a time-varying autoregressive signal demonstrate the effectiveness of the proposed algorithm.